def get_criterion(): if hasattr(nn, CFG.loss_name): return nn.__getattribute__(CFG.loss_name)(**CFG.loss_params) elif __CRITERIONS__.get(CFG.loss_name) is not None: return __CRITERIONS__[CFG.loss_name](**CFG.loss_params) else: raise NotImplementedError
def get_criterion(config: dict): loss_config = config["loss"] loss_name = loss_config["name"] loss_params = {} if loss_config.get("params") is None else loss_config.get( "params") if hasattr(nn, loss_name): criterion = nn.__getattribute__(loss_name)(**loss_params) else: criterion_cls = globals().get(loss_name) if criterion_cls is not None: criterion = criterion_cls(**loss_params) else: raise NotImplementedError return criterion
def get_criterion(config: dict): loss_config = config["loss"] loss_name = loss_config["name"] loss_params = loss_config["params"] if (loss_params is None) or (loss_params == ""): loss_params = {} if hasattr(nn, loss_name): criterion_ = nn.__getattribute__(loss_name)(**loss_params) else: criterion_cls = criterion.__getattribute__(loss_name) if criterion_cls is not None: criterion_ = criterion_cls(**loss_params) else: raise NotImplementedError return criterion_
def get_criterion(config: dict): loss_config = config["loss"] loss_name = loss_config["name"] return nn.__getattribute__(loss_name)()